# Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

arXiv: https://arxiv.org/abs/2105.05395 · HTML: https://mlsanity.com/paper/2105.05395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 7 of 7)

- [Learning Bayesian Networks: The Combination of Knowledge and Statistical Data](https://mlsanity.com/paper/1302.6815/) (2013)
- [Learning Gaussian Networks](https://mlsanity.com/paper/1302.6808/) (2013)
- [Estimating Treatment Effects with Causal Forests: An Application](https://mlsanity.com/paper/1902.07409/) (2019)
- [Using stacking to average Bayesian predictive distributions](https://mlsanity.com/paper/1704.02030/) (2017)
- [Bayesian model averaging: A systematic review and conceptual classification](https://mlsanity.com/paper/1509.08864/) (2015)
- [Learning Bayesian Networks: A Unification for Discrete and Gaussian Domains](https://mlsanity.com/paper/1302.4957/) (2013)
- [Variable Selection in Causal Inference using a Simultaneous Penalization Method](https://mlsanity.com/paper/1511.08501/) (2015)

## Abstract (arXiv)

Probabilistic machine learning models are often insufficient to help with decisions on interventions because those models find correlations - not causal relationships. If observational data is only available and experimentation are infeasible, the correct approach to study the impact of an intervention is to invoke Pearl's causality framework. Even that framework assumes that the underlying causal graph is known, which is seldom the case in practice. When the causal structure is not known, one may use out-of-the-box algorithms to find causal dependencies from observational data. However, there exists no method that also accounts for the decision-maker's prior knowledge when developing the causal structure either. The objective of this paper is to develop rational approaches for making decisions from observational data in the presence of causal graph uncertainty and prior knowledge from the decision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to infer set of causal graphs that can represent the data generation process. We provide decisions by computing the expected value and risk of potential interventions explicitly. We demonstrate our approach by applying them in different example contexts.